Master Degree / Yüksek Lisans Tezleri

Permanent URI for this collectionhttps://hdl.handle.net/11147/3008

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Author: search.filters.author.Tuğlular, TuğkanPublisher: search.filters.publisher.01. Izmir Institute of Technology

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  • Master Thesis
    Analysis of Test Smell Impact on Test Code Quality
    (01. Izmir Institute of Technology, 2024) Cebeci, İsmail; Tuğlular, Tuğkan; Tuğlular, Tuğkan; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    Test Kokuları, test kodundaki kalıplardır ve mutlaka yanlış olmasa da, test kodunun sürdürülebilirliğini ve etkililiğini engelleyebilecek kötü tasarım seçimlerini önerir. Yazılım geliştirmede, programlamada daha derin sorunlara işaret eden kod kokuları kavramından kaynaklanan test kokuları, benzer şekilde otomatik test komut dosyalarındaki, yazılım test sürecinin güvenilirliğini ve netliğini tehlikeye atabilecek sorunlara işaret eder. Bu tez içinde en çok bilinen 2 araç kullanarak (JNose and TestSmellDetector), GitHub üzerinden erişilen 500 proje incelenmiştir. Belirtilen 500 adet projelerde Java dili kullanılmasına dikkat edildi. İncelenen projelerde bulunan bütün test dosyaları, kullanılan 2 araç için input olarak kullanılmıştır. Araçların çıktıları karşılaştırılarak, toplam kaç adet test kokusu bulunduğu, hangi aracın hangi test kokularını daha iyi tespit ettiğini, en çok hangi test kokularının test dosyalarına etki ettiğini, test kokularının birbiriyle olan ilişkileri ve meydana gelme şıklıkları araştırılmıştır. Sonuç olarak 'Assertion Roulette,' 'Magic Number Test,' ve 'Lazy Test,' iki araç içinde en yaygın test kokuları olarak elde edilmiştir. Ek olarak, JNose aracı kullanılarak en yüksek birlikte gerçekleşme oranları 'Koşullu Test Mantığı' ile 'Hevesli Test' ve 'İstisna Yakalama Fırlatma' ile 'Bilinmeyen Test' arasında gözlemlenmiştir. Öte yandan, TestSmellDetector Aracı kullanıldığında en yüksek birliktelik oranları 'Bilinmeyen Test' ile 'Hevesli Test' ile 'Kaynak İyimserliği' ve 'Gizemli Misafir' arasında gözlenmiştir. Bu sonuçlar kullanılarak, test dosyaları üzerinde yeniden düzenleme işlemleri için ne tür çalışmalar yapılması gerektiği kolaylıkla belirlenebilir.
  • Master Thesis
    Development of a Machine Learning Platform for Analysis of Mitochondrial Features in Live-Cell Images
    (01. Izmir Institute of Technology, 2022) Tuğlular, Tuğkan; Tuğlular, Tuğkan; Tuğlular, Tuğkan; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    It is a laborious and error-prone manual process to mark the organelles in 2D and 3D images of living cells and identify the behavioral feedback to stimulations under measured conditions. This manual process can be simplified by being largely automated with machine learning techniques. We created a machine learning-based software platform named MitoML, which extracts sub-cellular structures, specifically mitochondria, and helps to identify the effects of external factors or changes under natural conditions. We investigate appropriate machine learning techniques for these objectives. Image processing and segmentation techniques with neural networks, enable researchers to carry out experiments with much better accuracy and a larger scale by automatically segmenting and counting the mitochondria, calculate the energy potentials based on region brightness. This way, analysis of mitochondria feedback in healthy and cancer cells under various conditions, such as nanomedicine and different treatment therapies, can be performed using MitoML. As a result of our work, we proposed a cascaded neural network architecture that can identify and count mitochondria, quantify its energy levels in fluorescence and other microscopy images, fast and at a standard reliable accuracy. Our test results outperformed the classical image processing algorithms provided in segmentation tools and software for medical image segmentation which was taken as a base line. Achieved accuracy rates 93.4% and %89.6 according to Dice and IoU metrics respectively are also better than any other related work encountered during the research. The proposed method can be improved to cover other sub-cellular structures relieving the researchers from non-standardized and laborious manual work which is prone to human error.
  • Master Thesis
    Mutation Analysis of Specification-Based Contracts in Software Testing [master Thesis]
    (01. Izmir Institute of Technology, 2021) Belli, Fevzi; Tuğlular, Tuğkan; Tuğlular, Tuğkan; Belli, Fevzi; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    Software used in fields such as medicine, finance, aviation and aerospace, nuclear power etc. is required to be reliable. Any software failures in these fields may have catastrophic consequences such as human and financial losses, which may cause a great damage to the economy and to social well-being. Hence, before launching, software should be rigorously tested. Testing can uncover the conditions, which software cannot handle. Those conditions might be overlooked during development. So, software testing points to the faults in the software under development to be patched. The important element of software testing is the use of the adequate test cases. If the outcome of the test case is positive, that means testing did not reveal any fault, then this test case might be considered as inefficient and useless for the tested version of software. Therefore, it is important to check test cases on adequacy, which can be achieved by mutation analysis. This thesis focuses on checking the adequacy of the test cases for Decision-Table-augmented Event Sequence Graphs (ESG-DTs) representation of a system under test by using mutation analysis. Test cases are represented in the Complete Event Sequence (CES) and Faulty CES (FCES) forms. This thesis presents a new set of mutation operators for mutation of contracts represented in Multi-Terminal Binary Decision Diagram (MTBDD). This thesis introduces a new approach for mutation of the ESG-DT model by using the proposed MTBDD mutation operators. The proposed approach is evaluated on three cases. The results for all cases show the drawback of specific FCES test sequences and the relationship between the mutant detection by CES/FCES sequences and proposed mutation operators.
  • Master Thesis
    Application of Graph Neural Networks on Software Modeling
    (01. Izmir Institute of Technology, 2020) Tuğlular, Tuğkan; Belli, Fevzi; Tuğlular, Tuğkan; Belli, Fevzi; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    Deficiencies and inconsistencies introduced during the modeling of software systems can cause undesirable consequences that may result in high costs and negatively affect the quality of all developments made using these models. Therefore, creating better models will help the software engineers to build better software systems that meet expectations. One of the software modelling methods used for analysis of graphical user interfaces is Event Sequence Graphs (ESG). The goal of this thesis is to propose a method that predicts missing or forgotten links between events defined in an ESG via Graph Neural Networks (GNN). A five-step process consisting of the following steps is proposed: (i) data collection from ESG model, (ii) dataset transformation, (iii) GNN model training, (iv) validation of trained model and (v) testing the model on unseen data. Three performance metrics, namely cross entropy loss, area under curve and accuracy, were used to measure the performance of the GNN models. Examining the results of the experiments performed on different datasets and different variations of GNN, shows that even with relatively small datasets prepared from ESG models, predicts missing or forgotten links between events defined in an ESG can be achieved.